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Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, Hubei, China
2
Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, Hubei, China
3
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, Hubei, China
4
The Collaborative Innovation Center for Geospatial Technology, Wuhan 430079, Hubei, China
5
The Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, Hubei, China
6
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1351; https://doi.org/10.3390/rs10091351
Received: 20 June 2018 / Revised: 7 August 2018 / Accepted: 21 August 2018 / Published: 24 August 2018
(This article belongs to the Special Issue Soil Moisture Remote Sensing Across Scales)
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Abstract

Soil moisture is a key component of the water cycle budget. Sensing soil moisture using microwave sensors onboard satellites is an effective way to retrieve surface soil moisture (SSM) at a global scale, but the retrieval accuracy in some regions is inadequate due to the complicated factors influencing the general retrieval process. On the other hand, monitoring soil moisture directly through in-situ devices is capable of providing high-accuracy SSM measurements, but the distribution of such stations is sparse. Recently, the Global Navigation Satellite System interferometric Reflectometry (GNSS-R) method was used to derive field-scale SSM, which can serve as a supplement to contemporary sparse in-situ soil moisture networks. On this basis, it is of great research significance to explore the fusion of these different kinds of SSM data, so as to improve the present satellite SSM products with regard to their data accuracy. In this paper, a multi-source point-surface fusion method based on the generalized regression neural network (GRNN) model is applied to fuse the Soil Moisture Active Passive (SMAP) Level 3 radiometer SSM daily product with in-situ measured and GNSS-R estimated SSM data from five soil moisture networks in the western continental U.S. The results show that the GRNN model obtains a fairly good performance, with a cross-validation R value of approximately 0.9 and a ubRMSE of 0.044 cm3 cm−3. Furthermore, the fused SSM product agrees well with the site-specific SSM data in terms of time and space, which demonstrates that the proposed GRNN model is able to construct the non-linear relationship between the point- and surface-scale SSM. View Full-Text
Keywords: soil moisture; multi-source data fusion; quality improvement; GRNN; microwave remote sensing; SMAP mission; GNSS-R soil moisture; multi-source data fusion; quality improvement; GRNN; microwave remote sensing; SMAP mission; GNSS-R
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Xu, H.; Yuan, Q.; Li, T.; Shen, H.; Zhang, L.; Jiang, H. Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S.. Remote Sens. 2018, 10, 1351.

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